Sridevi Krishnan

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• Nguyen, M. T., Krishnan, S., Phatak, S. V., & Karakas, S. E. (2023). Anti-Mullerian Hormone-Based Phenotyping Identifies Subgroups of Women with Polycystic Ovary Syndrome with Differing Clinical and Biochemical Characteristics. Diagnostics (Basel, Switzerland), 13(3).
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  Even though polycystic ovary syndrome (PCOS) was originally defined as "amenorrhea associated with bilateral polycystic ovaries", women without PCO morphology can be included in this diagnosis. This may contribute to the clinical heterogeneity seen in PCOS. Serum anti-Mullerian hormone (AMH) correlates with the number of ovarian cysts. We investigated whether phenotyping based on serum AMH can distinguish subgroups of PCOS with different clinical and biochemical characteristics. The electronic medical records of 108 women with PCOS (Rotterdam criteria) were reviewed. The serum AMH value correlated inversely (0.03 < < 0.0001) with age, weight, and BMI values and directly with serum total testosterone (T), free T, and bioavailable T values. When divided into quartiles based on serum AMH values, the women in the highest quartile (AMH: 18.5 ± 9.9 ng/mL; n = 27) had lower BMI (29.4 ± 6.9 vs. 34.0 ± 10.6-36.7 ± 7.2 kg/m) but higher total T (51.3 ± 27.2 vs. 26.5 ± 10.4-35.1 ± 16.3 ng/dL), free T (7.7 ± 6.0 vs. 4.4 ± 2.3-5.7 ± 3.2 ng/dL), and bioavailable T (22.1 ± 17.0 vs. 12.2 ± 6.6-16.5 ± 8.7 ng/dL) values. The combination of high AMH and high testosterone values may point to the ovaries and reproductive etiology for PCOS in this subgroup. Thus, AMH-based phenotyping may provide a practical and cost-effective tool to explore the heterogeneity in PCOS.
• Zhang, Y., Krishnan, S., Bao, B., Chiang, A. W., Sorrentino, J. T., Schinn, S. M., Kellman, B. P., & Lewis, N. E. (2023). Preparing glycomics data for robust statistical analysis with GlyCompareCT. STAR protocols, 4(2), 102162.
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  GlyCompareCT is a portable command-line tool to facilitate downstream glycomic data analyses, by addressing data inherent sparsity and non-independence. Inputting glycan abundances, users can run GlyCompareCT with one line of code to obtain the abundances of a minimal substructure set, named glycomotif, thereby quantifying hidden biosynthetic relationships between measured glycans. Optional parameters tuning and annotation are supported for personal preference. For complete details on the use and execution of this protocol, please refer to Bao et al. (2021)..
• Krishnan, S., Freytag, T., Jiang, X., Schuster, G. U., Woodhouse, L. R., Keim, N. L., & Stephensen, C. B. (2022). Effect of a diet based on the dietary guidelines for americans on inflammation markers in women at risk for cardiometabolic disease: results of a randomized, controlled trial. BMC nutrition, 8(1), 157.
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  To evaluate the effect of a diet pattern based on Dietary Guidelines for Americans (DGA), in a controlled feeding setting, on plasma markers of inflammation and on cytokine production by peripheral blood mononuclear cells (PBMC).
• Krishnan, S., Gertz, E. R., Adams, S. H., Newman, J. W., Pedersen, T. L., Keim, N. L., & Bennett, B. J. (2022). Effects of a diet based on the Dietary Guidelines on vascular health and TMAO in women with cardiometabolic risk factors. Nutrition, metabolism, and cardiovascular diseases : NMCD, 32(1), 210-219.
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  Recent evidence links trimethylamine oxide (TMAO) to endothelial dysfunction, an early indicator of cardiovascular disease. We aimed to determine whether short-term consumption of a diet patterned after the 2010 Dietary Guidelines for Americans (DGA) would affect endothelial function, plasma TMAO concentrations, and cardiovascular disease risk, differently than a typical American Diet (TAD).
• Newman, J. W., Krishnan, S., Borkowski, K., Adams, S. H., Stephensen, C. B., & Keim, N. L. (2022). Assessing Insulin Sensitivity and Postprandial Triglyceridemic Response Phenotypes With a Mixed Macronutrient Tolerance Test. Frontiers in nutrition, 9, 877696.
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  The use of meal challenge tests to assess postprandial responses in carbohydrate and fat metabolism is well established in clinical nutrition research. However, challenge meal compositions and protocols remain a variable. Here, we validated a mixed macronutrient tolerance test (MMTT), containing 56-g palm oil, 59-g sucrose, and 26-g egg white protein for the parallel determination of insulin sensitivity and postprandial triglyceridemia in clinically healthy subjects. The MMTT was administered in two study populations. In one, women with overweight/obese BMIs ( = 43) involved in an 8-week dietary intervention were administered oral glucose tolerance tests (OGTTs) and MMTTs within 2 days of each other after 0, 2, and 8 weeks of the dietary intervention. In the other, 340 men and women between 18 and 64 years of age, with BMI from 18-40 kg/m, completed the MMTT as part of a broad nutritional phenotyping effort. Postprandial blood collected at 0, 0.5, 3, and 6 h was used to measure glucose, insulin, and clinical lipid panels. The MMTT postprandial insulin-dependent glucose disposal was evaluated by using the Matsuda Index algorithm and the 0- and 3 h blood insulin and glucose measures. The resulting MMTT insulin sensitivity index (ISI) was strongly correlated ( = 0.77, < 0.001) with the OGTT-dependent 2 h composite Matsuda index (ISI), being related by the following equation: Log (ISI) = [0.8751 x Log(ISI)] -0.2115. An area under the triglyceride excursion curve >11.15 mg/mL h calculated from the 0, 3, and 6 h blood draws established mild-to-moderate triglyceridemia in agreement with ∼20% greater prevalence of hypertriglyceridemia than fasting indications. We also demonstrated that the product of the 0 to 3 h and 3 to 6 h triglyceride rate of change as a function of the triglyceride incremental area under the curve optimally stratified subjects by postprandial response patterns. Notably, ∼2% of the population showed minimal triglyceride appearance by 6 h, while ∼25% had increasing triglycerides through 6 h. Ultimately, using three blood draws, the MMTT allowed for the simultaneous determination of insulin sensitivity and postprandial triglyceridemia in individuals without clinically diagnosed disease.
• Richardson, C. E., Krishnan, S., Gray, I. J., Keim, N. L., & Newman, J. W. (2022). The Omega-3 Index Response to an 8 Week Randomized Intervention Containing Three Fatty Fish Meals Per Week Is Influenced by Adiposity in Overweight to Obese Women. Frontiers in nutrition, 9, 810003.
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  The Dietary Guidelines for Americans (DGA) recommends consuming ~225 g/wk of a variety of seafood providing >1.75 g/wk of long-chain omega-3 fatty acids to reduce cardiovascular disease risk, however individual responses to treatment vary.
• Tacad, D. K., Tovar, A. P., Richardson, C. E., Horn, W. F., Keim, N. L., Krishnan, G. P., & Krishnan, S. (2022). Satiety Associated with Calorie Restriction and Time-Restricted Feeding: Central Neuroendocrine Integration. Advances in nutrition (Bethesda, Md.), 13(3), 758-791.
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  This review focuses on summarizing current knowledge on how time-restricted feeding (TRF) and continuous caloric restriction (CR) affect central neuroendocrine systems involved in regulating satiety. Several interconnected regions of the hypothalamus, brainstem, and cortical areas of the brain are involved in the regulation of satiety. Following CR and TRF, the increase in hunger and reduction in satiety signals of the melanocortin system [neuropeptide Y (NPY), proopiomelanocortin (POMC), and agouti-related peptide (AgRP)] appear similar between CR and TRF protocols, as do the dopaminergic responses in the mesocorticolimbic circuit. However, ghrelin and leptin signaling via the melanocortin system appears to improve energy balance signals and reduce hyperphagia following TRF, which has not been reported in CR. In addition to satiety systems, CR and TRF also influence circadian rhythms. CR influences the suprachiasmatic nucleus (SCN) or the primary circadian clock as seen by increased clock gene expression. In contrast, TRF appears to affect both the SCN and the peripheral clocks, as seen by phasic changes in the non-SCN (potentially the elusive food entrainable oscillator) and metabolic clocks. The peripheral clocks are influenced by the primary circadian clock but are also entrained by food timing, sleep timing, and other lifestyle parameters, which can supersede the metabolic processes that are regulated by the primary circadian clock. Taken together, TRF influences hunger/satiety, energy balance systems, and circadian rhythms, suggesting a role for adherence to CR in the long run if implemented using the TRF approach. However, these suggestions are based on only a few studies, and future investigations that use standardized protocols for the evaluation of the effect of these diet patterns (time, duration, meal composition, sufficiently powered) are necessary to verify these preliminary observations.
• Tacad, D. K., Tovar, A. P., Richardson, C. E., Horn, W. F., Krishnan, G. P., Keim, N. L., & Krishnan, S. (2022). Satiety Associated with Calorie Restriction and Time-Restricted Feeding: Peripheral Hormones. Advances in nutrition (Bethesda, Md.), 13(3), 792-820.
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  Calorie restriction (CR) is a common approach to inducing negative energy balance. Recently, time-restricted feeding (TRF), which involves consuming food within specific time windows during a 24-h day, has become popular owing to its relative ease of practice and potential to aid in achieving and maintaining a negative energy balance. TRF can be implemented intentionally with CR, or TRF might induce CR simply because of the time restriction. This review focuses on summarizing our current knowledge on how TRF and continuous CR affect gut peptides that influence satiety. Based on peer-reviewed studies, in response to CR there is an increase in the orexigenic hormone ghrelin and a reduction in fasting leptin and insulin. There is likely a reduction in glucagon-like peptide-1 (GLP-1), peptide YY (PYY), and cholecystokinin (CCK), albeit the evidence for this is weak. After TRF, unlike CR, fasting ghrelin decreased in some TRF studies, whereas it showed no change in several others. Further, a reduction in fasting leptin, insulin, and GLP-1 has been observed. In conclusion, when other determinants of food intake are held equal, the peripheral satiety systems appear to be somewhat similarly affected by CR and TRF with regard to leptin, insulin, and GLP-1. But unlike CR, TRF did not appear to robustly increase ghrelin, suggesting different influences on appetite with a potential decrease of hunger after TRF when compared with CR. However, there are several established and novel gut peptides that have not been measured within the context of CR and TRF, and studies that have evaluated effects of TRF are often short-term, with nonuniform study designs and highly varying temporal eating patterns. More evidence and studies addressing these aspects are needed to draw definitive conclusions.
• Artegoitia, V. M., Krishnan, S., Bonnel, E. L., Stephensen, C. B., Keim, N. L., & Newman, J. W. (2021). Healthy eating index patterns in adults by sex and age predict cardiometabolic risk factors in a cross-sectional study. BMC nutrition, 7(1), 30.
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  Associations between diet and cardiometabolic disease (CMD) risk may vary in men and women owing to sex differences in eating habits and physiology. The current secondary analysis sought to determine the ability of sex differences in dietary patterns to discriminate groups with or without CMD risk factors (CMDrf) in the adult population and if this was influenced by age.
• Kim, T., Xie, Y., Li, Q., Artegoitia, V. M., Lebrilla, C. B., Keim, N. L., Adams, S. H., & Krishnan, S. (2021). Diet affects glycosylation of serum proteins in women at risk for cardiometabolic disease. European journal of nutrition, 60(7), 3727-3741.
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  Glycoproteomics deals with glycoproteins that are formed by post-translational modification when sugars (like fucose and sialic acid) are attached to protein. Glycosylation of proteins influences function, but whether glycosylation is altered by diet is unknown.
• Krishnan, S., & Krishnan, G. P. (2021). N-Glycosylation Network Construction and Analysis to Modify Glycans on the Spike (S) Glycoprotein of SARS-CoV-2. Frontiers in bioinformatics, 1, 667012.
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  The N-glycan structure and composition of the spike (S) protein of SARS-CoV-2 are pertinent to vaccine development and efficacy. We reconstructed the glycosylation network based on previously published mass spectrometry data using GNAT, a glycosylation network analysis tool. Our compilation of the network tool had 26 glycosyltransferase and glucosidase enzymes and could infer the pathway of glycosylation machinery based on glycans in the virus spike protein. Once the glycan biosynthesis pathway was generated, we simulated the effect of blocking specific enzymes-swainsonine or deoxynojirimycin for blocking mannosidase-II and indolizidine for blocking alpha-1,6-fucosyltransferase-to see how they would affect the biosynthesis network and the glycans that were synthesized. The N-glycan biosynthesis network of SARS-CoV-2 spike protein shows an elaborate enzymatic pathway with several intermediate glycans, along with the ones identified by mass spectrometric studies. Of the 26 enzymes, the following were involved-Man-Ia, MGAT1, MGAT2, MGAT4, MGAT5, B3GalT, B4GalT, Man-II, SiaT, ST3GalI, ST3GalVI, and FucT8. Blocking specific enzymes resulted in a substantially modified glycan profile of SARS-CoV-2. Variations in the final N-glycan profile of the virus, given its site-specific microheterogeneity, are factors in the host response to the infection, vaccines, and antibodies. Heterogeneity in the N-glycan profile of the spike (S) protein and its potential effect on vaccine efficacy or adverse reactions to the vaccines remain unexplored. Here, we provide all the resources we generated-the glycans in the glycoCT xml format and the biosynthesis network for future work.
• Krishnan, S., Adams, S. H., Witbracht, M. G., Woodhouse, L. R., Piccolo, B. D., Thomas, A. P., Souza, E. C., Horn, W. F., Gertz, E. R., Van Loan, M. D., & Keim, N. L. (2021). Weight Loss, but Not Dairy Composition of Diet, Moderately Affects Satiety and Postprandial Gut Hormone Patterns in Adults. The Journal of nutrition, 151(1), 245-254.
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  Inclusion of dairy in diet patterns has been shown to have mixed effects on weight loss. A prevailing hypothesis is that dairy improves weight loss by influencing endocrine systems associated with satiety and food intake regulation.
• Krishnan, S., O'Connor, L. E., Wang, Y., Gertz, E. R., Campbell, W. W., & Bennett, B. J. (2021). Adopting a Mediterranean-style eating pattern with low, but not moderate, unprocessed, lean red meat intake reduces fasting serum trimethylamine N-oxide (TMAO) in adults who are overweight or obese. The British journal of nutrition, 128(9), 1-21.
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  A Mediterranean-style eating pattern (MED-EP) may include moderate red meat intake. However, it is unknown if the pro-atherogenic metabolite trimethylamine N-oxide (TMAO) is affected by the amount of red meat consumed with a MED-EP. The results presented are from a secondary, retrospective objective of an investigator-blinded, randomized, crossover, controlled feeding trial (two 5-wk interventions separated by a 4-wk washout) to determine if a MED-EP with 200g unprocessed lean red meat/wk (MED-CONTROL) reduces circulating TMAO concentrations compared to a MED-EP with 500g unprocessed lean red meat/wk (MED-RED). Participants were 27 women and 12 men (n=39 total) who were either overweight or obese (BMI: 30.5 ± 0.3 kg/m2 mean ± SEM). Serum samples were obtained following an overnight fast both before (pre) and after (post) each intervention. Fasting serum TMAO, choline, carnitine, and betaine concentrations were measured using a targeted Liquid chromatography-mass spectrometry. Data were analyzed to assess if (a) TMAO and related metabolites differed by intervention, and (b) if changes in TMAO were associated with changes in Framingham 10-year risk score. Serum TMAO was lower post-intervention following MED-CONTROL compared to MED-RED intervention (post-MED-CONTROL 3.1 ± 0.2 µM vs. post-MED-RED 5.0 ± 0.5 µM, p
• Krishnan, S., & Ramyaa, R. (2020). When two heads are better than one: nutritional epidemiology meets machine learning. The American journal of clinical nutrition, 111(6), 1124-1126.
• Krishnan, S., Lee, F., Burnett, D. J., Kan, A., Bonnel, E. L., Allen, L. H., Adams, S. H., & Keim, N. L. (2020). Challenges in Designing and Delivering Diets and Assessing Adherence: A Randomized Controlled Trial Evaluating the 2010 Dietary Guidelines for Americans. Current developments in nutrition, 4(3), nzaa022.
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  Controlled-feeding trials are challenging to design and administer in a free-living setting. There is a need to share methods and best practices for diet design, delivery, and standard adherence metrics.
• Ramyaa, R., Hosseini, O., Krishnan, G. P., & Krishnan, S. (2019). Phenotyping Women Based on Dietary Macronutrients, Physical Activity, and Body Weight Using Machine Learning Tools. Nutrients, 11(7).
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  Nutritional phenotyping can help achieve personalized nutrition, and machine learning tools may offer novel means to achieve phenotyping. The primary aim of this study was to use energy balance components, namely input (dietary energy intake and macronutrient composition) and output (physical activity) to predict energy stores (body weight) as a way to evaluate their ability to identify potential phenotypes based on these parameters. From the Women's Health Initiative Observational Study (WHI OS), carbohydrates, proteins, fats, fibers, sugars, and physical activity variables, namely energy expended from mild, moderate, and vigorous intensity activity, were used to predict current body weight (both as body weight in kilograms and as a body mass index (BMI) category). Several machine learning tools were used for this prediction. Finally, cluster analysis was used to identify putative phenotypes. For the numerical predictions, the support vector machine (SVM), neural network, and k-nearest neighbor (kNN) algorithms performed modestly, with mean approximate errors (MAEs) of 6.70 kg, 6.98 kg, and 6.90 kg, respectively. For categorical prediction, SVM performed the best (54.5% accuracy), followed closely by the bagged tree ensemble and kNN algorithms. K-means cluster analysis improved prediction using numerical data, identified 10 clusters suggestive of phenotypes, with a minimum MAE of ~1.1 kg. A classifier was used to phenotype subjects into the identified clusters, with MAEs
• Forester, S. M., Widaman, A. M., Krishnan, S., Witbracht, M. G., Horn, W. F., Laugero, K. D., & Keim, N. L. (2018). A Clear Difference Emerges in Hormone Patterns Following a Standard Midday Meal in Young Women Who Regularly Eat or Skip Breakfast. The Journal of nutrition, 148(5), 685-692.
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  Multiple hormones are involved in the regulation of food intake and glucose metabolism. Past intervention studies showed a benefit of eating breakfast on satiety, but this was possibly confounded by the disruption of habitual meal patterns.
• Krishnan, S., Adams, S. H., Allen, L. H., Laugero, K. D., Newman, J. W., Stephensen, C. B., Burnett, D. J., Witbracht, M., Welch, L. C., Que, E. S., & Keim, N. L. (2018). A randomized controlled-feeding trial based on the Dietary Guidelines for Americans on cardiometabolic health indexes. The American journal of clinical nutrition, 108(2), 266-278.
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  The 2010 Dietary Guidelines for Americans (DGA) recommend nutrient needs be met by increasing fruit, vegetable, and whole-grain intake with the use of low-fat or fat-free dairy products and by reducing sodium, solid fats, and added sugars. However, the DGA, as a dietary pattern, have not been tested in an intervention trial.
• Krishnan, S., Agrawal, K., Tryon, R. R., Welch, L. C., Horn, W. F., Newman, J. W., & Keim, N. L. (2018). Structural equation modeling of food craving across the menstrual cycle using behavioral, neuroendocrine, and metabolic factors. Physiology & behavior, 195, 28-36.
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  To identify associations between circulating endocannabinoids and craving during the luteal phase of the menstrual cycle. This report is a secondary analysis of a trial registered in clinicaltrials.gov as NCT01407692.
• Mangrola, D., Cox, C., Furman, A. S., Krishnan, S., & Karakas, S. E. (2018). SELF BLOOD GLUCOSE MONITORING UNDERESTIMATES HYPERGLYCEMIA AND HYPOGLYCEMIA AS COMPARED TO CONTINUOUS GLUCOSE MONITORING IN TYPE 1 AND TYPE 2 DIABETES. Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists, 24(1), 47-52.
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  When glucose records from self blood glucose monitoring (SBGM) do not reflect estimated average glucose from glycosylated hemoglobin (HgBA1) or when patients' clinical symptoms are not explained by their SBGM records, clinical management of diabetes becomes a challenge. Our objective was to determine the magnitude of differences in glucose values reported by SBGM versus those documented by continuous glucose monitoring (CGM).
• Davis, J. C., Lewis, Z. T., Krishnan, S., Bernstein, R. M., Moore, S. E., Prentice, A. M., Mills, D. A., Lebrilla, C. B., & Zivkovic, A. M. (2017). Growth and Morbidity of Gambian Infants are Influenced by Maternal Milk Oligosaccharides and Infant Gut Microbiota. Scientific reports, 7, 40466.
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  Human milk oligosaccharides (HMOs) play an important role in the health of an infant as substrate for beneficial gut bacteria. Little is known about the effects of HMO composition and its changes on the morbidity and growth outcomes of infants living in areas with high infection rates. Mother's HMO composition and infant gut microbiota from 33 Gambian mother/infant pairs at 4, 16, and 20 weeks postpartum were analyzed for relationships between HMOs, microbiota, and infant morbidity and growth. The data indicate that lacto-N-fucopentaose I was associated with decreased infant morbidity, and 3'-sialyllactose was found to be a good indicator of infant weight-for-age. Because HMOs, gut microbiota, and infant health are interrelated, the relationship between infant health and their microbiome were analyzed. While bifidobacteria were the dominant genus in the infant gut overall, Dialister and Prevotella were negatively correlated with morbidity, and Bacteroides was increased in infants with abnormal calprotectin. Mothers nursing in the wet season (July to October) produced significantly less oligosaccharides compared to those nursing in the dry season (November to June). These results suggest that specific types and structures of HMOs are sensitive to environmental conditions, protective of morbidity, predictive of growth, and correlated with specific microbiota.
• Hawkins, K. R., Krishnan, S., Ringos, L., Garcia, V., & Cooper, J. A. (2017). Running Performance With Nutritive and Nonnutritive Sweetened Mouth Rinses. International journal of sports physiology and performance, 12(8), 1105-1110.
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  Using mouth rinse (MR) with carbohydrate during exercise has been shown to act as an ergogenic aid.
• Krishnan, S., Shimoda, M., Sacchi, R., Kailemia, M. J., Luxardi, G., Kaysen, G. A., Parikh, A. N., Ngassam, V. N., Johansen, K., Chertow, G. M., Grimes, B., Smilowitz, J. T., Maverakis, E., Lebrilla, C. B., & Zivkovic, A. M. (2017). HDL Glycoprotein Composition and Site-Specific Glycosylation Differentiates Between Clinical Groups and Affects IL-6 Secretion in Lipopolysaccharide-Stimulated Monocytes. Scientific reports, 7, 43728.
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  The goal of this pilot study was to determine whether HDL glycoprotein composition affects HDL's immunomodulatory function. HDL were purified from healthy controls (n = 13), subjects with metabolic syndrome (MetS) (n = 13), and diabetic hemodialysis (HD) patients (n = 24). Concentrations of HDL-bound serum amyloid A (SAA), lipopolysaccharide binding protein (LBP), apolipoprotein A-I (ApoA-I), apolipoprotein C-III (ApoC-III), α-1-antitrypsin (A1AT), and α-2-HS-glycoprotein (A2HSG); and the site-specific glycovariations of ApoC-III, A1AT, and A2HSG were measured. Secretion of interleukin 6 (IL-6) in lipopolysaccharide-stimulated monocytes was used as a prototypical assay of HDL's immunomodulatory capacity. HDL from HD patients were enriched in SAA, LBP, ApoC-III, di-sialylated ApoC-III (ApoC-III) and desialylated A2HSG. HDL that increased IL-6 secretion were enriched in ApoC-III, di-sialylated glycans at multiple A1AT glycosylation sites and desialylated A2HSG, and depleted in mono-sialylated ApoC-III (ApoC-III). Subgroup analysis on HD patients who experienced an infectious hospitalization event within 60 days (HD+) (n = 12), vs. those with no event (HD-) (n = 12) showed that HDL from HD+ patients were enriched in SAA but had lower levels of sialylation across glycoproteins. Our results demonstrate that HDL glycoprotein composition, including the site-specific glycosylation, differentiate between clinical groups, correlate with HDL's immunomodulatory capacity, and may be predictive of HDL's ability to protect from infection.
• Krishnan, S., Steffen, L. M., Paton, C. M., & Cooper, J. A. (2017). Impact of dietary fat composition on prediabetes: a 12-year follow-up study. Public health nutrition, 20(9), 1617-1626.
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  Dietary fatty acid composition likely affects prediabetic conditions such as isolated impaired fasting glucose (IFG) or impaired glucose tolerance (IGT); however, this risk has not been evaluated in a large population nor has it been followed prospectively.